Method and system for automatically identifying related content to an electronic text

ABSTRACT

The exemplary embodiments provide methods and systems for automatically identifying content related to an electronic text. Aspects of exemplary embodiments include linking topic categories, psychological states, demographic profiles, and additional content using one or more databases; in response to receiving content of an electronic text, analyzing by a software component executing on a computer the content and assigning one or more of topic categories to the content; automatically identifying at least one the psychological states of a user caused by the content and the demographic profiles whose members would be interested in the content that are linked to the one or more topic categories assigned to the content; and presenting a portion of the additional content that is linked to at least one of the identified demographic profiles and the psychological states.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/637,558, filed Dec. 14, 2009, assigned to the assignee of the presentapplication, and incorporated herein by reference.

BACKGROUND

Online advertising is becoming growingly more valuable and leadingbrands are increasing their budget to advertise on the Web. However,investing more in advertising does not mean automatically making moreprofit on the advertised products and services. If an ad is not relatedto the contents of the webpage where the ad is placed, reaching thedesired target becomes more difficult.

Some conventional systems use keyword analysis to place advertisements.But such systems can be unreliable and imprecise, and the results can beinsignificant or even counterproductive. For example consider a newsarticle about a hurricane in the Caribbean. Based on keyword analysis, aconventional system may associate with that article an ad for aCaribbean vacation package, which could be counterproductive.

Accordingly, a need exists for a tool that processes the meaning of textto understand the interests and needs of readers of the text andautomatically identify related content based on those interests andneeds.

BRIEF SUMMARY

The exemplary embodiments provide methods and systems for automaticallyidentifying content related to an electronic text. Aspects of exemplaryembodiments include linking topic categories, psychological states,demographic profiles, and additional content using one or moredatabases; in response to receiving content of an electronic text,analyzing by a software component executing on a computer the contentand assigning one or more of topic categories to the content;automatically identifying at least one the psychological states of auser caused by the content and the demographic profiles whose memberswould be interested in the content that are linked to the one or moretopic categories assigned to the content; and presenting a portion ofthe additional content that is linked to at least one of the identifieddemographic profiles and the psychological states.

According to the method and system disclosed herein, the exemplaryembodiment processes the meaning of the text to understand the interestsand needs of readers of the text and automatically identifies relatedcontent based on those interests and needs as identified in selecteddemographic profiles and/or psychological states.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIGS. 1A and 1B are diagrams illustrating embodiments of a semanticadvertiser system 10 for automatically identifying a demographic profileand psychological state of a reader of electronic text.

FIG. 2 is a flow diagram illustrating one embodiment a process forautomatically identifying content related to an electronic text.

FIGS. 3A-3D are block diagrams illustrating three embodiments forlinking topic categories, psychological states, demographic profiles,and additional content using one or more databases.

FIG. 4 is a block diagram graphically illustrating contents of thecontextual taxonomy.

FIG. 5 is a diagram illustrating an example database schema according tothe first embodiment.

FIG. 6 is a diagram illustrating a process of automatically identifyingcontent related to an electronic text in accordance with the firstembodiment.

FIG. 7 is a diagram illustrating a process of automatically identifyingcontent related to an electronic text in accordance with the secondembodiment.

FIG. 8 is a diagram illustrating a process of automatically identifyingcontent related to an electronic text in accordance with the thirdembodiment.

DETAILED DESCRIPTION

The exemplary embodiment relates to automatically identifying contentrelated to an electronic text. The following description is presented toenable one of ordinary skill in the art to make and use the inventionand is provided in the context of a patent application and itsrequirements. Various modifications to the exemplary embodiments and thegeneric principles and features described herein will be readilyapparent. The exemplary embodiments are mainly described in terms ofparticular methods and systems provided in particular implementations.However, the methods and systems will operate effectively in otherimplementations. Phrases such as “exemplary embodiment”, “oneembodiment” and “another embodiment” may refer to the same or differentembodiments. The embodiments will be described with respect to systemsand/or devices having certain components. However, the systems and/ordevices may include more or less components than those shown, andvariations in the arrangement and type of the components may be madewithout departing from the scope of the invention. The exemplaryembodiments will also be described in the context of particular methodshaving certain steps. However, the method and system operate effectivelyfor other methods having different and/or additional steps and steps indifferent orders that are not inconsistent with the exemplaryembodiments. Thus, the present invention is not intended to be limitedto the embodiments shown, but is to be accorded the widest scopeconsistent with the principles and features described herein.

The exemplary embodiments provide a method and system for automaticallyidentifying content related to an electronic text based on a contextualtaxonomy, a psychological state of a reader caused by the content anddemographic profiles of readers. The system analyzes and automaticallyidentifies the topic categories discussed in the electronic text, and byinteracting with an additional content database, such as an advertisingdatabase, selects the most relevant content, such as ads, to bedisplayed with the text through identification of psychological state ofa reader caused by the text and demographic profiles that would beinterested in the content, thereby offering highly related content tothe reader.

FIG. 1A is a diagram illustrating one embodiment of a semanticadvertiser system 10 that automatically identifies content related to anelectronic text. The semantic advertiser system 10 may include asemantic engine 12 and an analysis engine 14 executing on a server 16,and a data repository 18. The data repository 18 may comprise a contenttaxonomy 20, a demographic profile database 22, a psychological statemap 24, and an additional attributes database 27. The semanticadvertiser system 10 may also utilize an additional content database 26and an additional content inventory 34 that in one embodiment may becontrolled or operated by a third party entity 36.

In one embodiment, the contextual taxonomy 20 may be structured as ahierarchy of topic categories found in text. The contextual taxonomy 20may be used by the semantic engine 12 to classify content of anelectronic text 28 into one or more topic categories based on thecontextual taxonomy 20. In one embodiment, the contextual taxonomy 20may be optimized for a particular type of electronic text content, andthe semantic advertiser service 10 may include multiple versions of thecontextual taxonomy 20, each optimized for different types of electronictexts 28. In one embodiment, the contextual taxonomy 20 may link generictopic categories in the form of domains and/or concepts from a semanticnetwork 13 to one or more granular topic categories contained in thecontextual taxonomy 20.

The additional attributes database 27 may include attributes that can beassociated with particular granular topic categories or entities such aspeople, organizations, geographic location, and sentiment. For instance,example attributes that may be associated with a concept of “President”,for example, may include “Success”, “mediocre” and “failure”.

The psychological state map 24 may include a plurality of recordsrepresenting human mental states and attributes thereof. Thepsychological state map 24 may map each psychological state containedtherein to a set of psychological state needs, which are wants ordesires stemming from the corresponding psychological state. In oneembodiment, the psychological state map 24 may link generic topiccategories in the form of domains and/or concepts from the semanticnetwork 13 to one or more psychological states contained in thepsychological state map 24.

The demographic profile database 22 may include plurality of demographicprofiles, each of which represents a subgroup of a population and thatincludes information about members of the subgroup that may be usefulfor marketing purposes. In one embodiment, the demographic profiledatabase 22 may link of one or more of the granular topic categoriesfrom the contextual taxonomy 20 to predetermined demographic profilesfrom the demographic profile database 22. In this embodiment, each ofthe demographic profiles may include a weight indicating a level ofrelative importance of the demographic profile to the granular topiccategories among other attributes, and each of the demographic profilesmay further include a link to a set of profile needs that signify wantsor desires of the members of the corresponding demographic profile.

The additional content database 26 may be populated with content thatcan be associated with the electronic text 28 based on the outcome ofthe semantic analysis performed by the semantic advertiser service 10.

The server 16 may be configured to receive as input the electronic text28. In one embodiment, the electronic text 28 may comprise a web pagefrom a document repository 30. In one embodiment, the documentrepository 30 may represent a web server and/or a database containingelectronic texts 28 such as web pages. In another embodiment, thedocument repository may represent any type of electronic storagesuitable for storing electronic texts 28 containing textual informationin any type of digital file format. In one embodiment, example types ofelectronic texts may include documents in which additional content, suchas advertisements, may be displayed including web pages, articles,blogs, publications, e-mails, text messages, tweets, and the like. Inone embodiment, the electronic text 28 may be served simultaneously toboth the semantic advertiser service 10 and to a user 32 (e.g., a websurfer) via an electronic device display (not shown). In one embodiment,the document repository 30 may be provided by a third party entity 36.

In one embodiment, the semantic advertiser system 10 may be used toidentify topic categories included in the input electronic text 28, andautomatically identify a psychological state of the user 32 caused bythe content of the electronic text 28 and a demographic profile thatwould find the content relevant. The identified demographic profile andthe psychological state of the user, one or both of which may beassociated with a hierarchy of needs, may then be used to present theuser with additional content 30 specifically targeted to addressingneeds associated with the psychological state and/or the demographicprofile of the user at that moment.

In another embodiment, the semantic advertiser system 10 may be used bythe third party entity 36 to identify demographic profiles andpsychological states of potential readers of the electronic text 28 toselect additional content 33 related to the electronic text 28 thatwould derive the maximize income from placement of the additionalcontent 30. The server 16 may also be configured to access or receive anadditional content inventory 34. The additional content inventory 34 maycomprise advertisements or other content that can be related to thecontent of the electronic text 28. The additional content inventory 34may be used to populate the additional content database 26 and may alsobe provided by the same or different third party entity 36. In oneembodiment, the third party entity 36 may represent a content publisher,an advertiser or an ad agency, for example. In one embodiment,third-party entity 36 may make the additional content inventory 34, theadditional content database 26 and/or the document repository 30available to the semantic advertiser service 10 via a network 37 (e.g.,the Internet) for automatic placement of ads within the electronictext(s) 28.

In an alternative embodiment, the semantic advertiser service 10 may beimplemented as a desktop application that runs on a PC. In this case,the electronic text 28 and the additional content inventory 34 may beobtained from an internal or external drive of the PC, or an externalserver, or a combination thereof.

The server 16 may include hardware components of typical computingdevices (not shown), including a processor, input devices (e.g.,keyboard, pointing device, microphone for voice commands, buttons,touchscreen, etc.), output devices (e.g., a display device, speakers,and the like). The server 16 may include computer-readable media, e.g.,memory and storage devices (e.g., flash memory, hard drive, optical diskdrive, magnetic disk drive, and the like) containing computerinstructions that implement the functionality disclosed when executed bythe processor. The server 16 may further include wired or wirelessnetwork communication interfaces for communication. Although the server16 is shown as a single computer, it should be understood that thefunctions of server 16 may be distributed over more than one server, andthe functionality of software components may be implemented using adifferent number of software components.

Although the semantic engine 12 and an analysis engine 14 are shown assoftware components, in another embodiment the semantic engine 12 andthe analysis engine 14 may be implemented in hardware. In addition, thefunctionality of the semantic engine 12 and the analysis engine 14 maybe combined into a lesser or greater number of modules/components thatare run on any type of computing device.

FIG. 2 is a flow diagram illustrating one embodiment of a process forautomatically identifying content related to an electronic text. Theprocess may include linking the topic categories, the psychologicalstates, the demographic profiles, and the additional content using oneor more databases (block 200). In one embodiment, the topic categories,the psychological states 42, the demographic profiles 40, and theadditional content are linked via the contextual taxonomy 20, thedemographic profile database 22, the psychological state map 24, and theadditional content database 26, as described in various embodimentsfurther below.

In response to receiving content of an electronic text 28, the semanticadvertiser system 10 analyzes the content and assigns one or more of thetopic categories to the content (block 202).

According to one exemplary embodiment, the semantic advertiser system 10may provide different levels of topic categories to enable deeperunderstanding of the electronic text 28, as shown in FIG. 1B.

FIG. 1B is a diagram illustrating one embodiment of a semanticadvertiser system 10 in further detail. In this embodiment, the semanticengine 12 may generate both generic topic categories 38A from thesemantic network 13 and granular topic categories 38B from thecontextual taxonomy 20. In one embodiment, the semantic engine 12 mayperform the semantic analysis on the content using the semantic network13 and assign generic topic categories 38A identifying generic domains,main concepts, and entities included in the electronic text 28. Thesemantic network 13 may be implemented as described in U.S. Pat. No.7,899,666 B2 entitled “Method And System For Automatically ExtractingRelations Between Concepts Included In Text” issued Mar. 1, 2011, andincorporated herein by reference.

In one embodiment, the extracted generic domains, main concepts, andentities may form a generic content map for the electronic text 28 thatis used to access the contextual taxonomy 20, the other attributesdatabase 27, and the psychological state map 24. The semantic engine 12may use the generic content map to automatically identify granular topiccategories 38B from the contextual taxonomy 20, as well as otherattributes 48 from the other attributes database 27 that are included inthe input electronic text 28.

As an example, assume the semantic engine 12 analyzes an article aboutBarack Obama throwing out a first pitch at a major league baseball game.The generic topic categories 38A returned from the semantic network 13may include a generic domain of “sport” and main concepts of“President”, “Barack Obama”, “MLB”, “game”, and the like. The granulartopic categories 38B returned from the contextual taxonomy 20, however,may be more specific, such as “Baseball.Exhibition.Game” and“Politics.Internal-affair”.

The generic topic categories 38A and the granular topic categories 38Bmay be collectively referred to as topic categories 38.

Referring again to FIG. 2, the semantic advertiser system 10automatically identifies at least one of the psychological states 42 ofa user caused by the content and the demographic profiles 40 whosemembers would be interested in the content that are linked to the one ormore topic categories 38 assigned to the content (block 204). As usedherein, identifying at least one of the demographic profiles 40 and atleast one of the psychological states 42 may mean identifying at leastone of the demographic profiles 40 only; identifying at least one of thepsychological states 42 only; or identifying at least one of each.

The semantic advertiser system 10 then presents a portion of theadditional content that is linked to the identified demographic profiles40 and/or the psychological states 42 (block 206). In one embodiment,the additional content 33 may be presented by displaying the additionalcontent 33 within, or in association with, the electronic text 28 inreal-time. In another embodiment, presenting may include providing apointer (e.g., a URL) to the additional content 33. Although in oneembodiment, the additional content 33 comprises advertisements, theadditional content 33 may comprises other types of media including,articles, maps, pictures and videos, for example.

As an example of the operation of the semantic advertiser system 10consider the example of the news article about a hurricane in theCaribbean. Based on the semantic analysis, the semantic advertisersystem 10 may determine that generic topic categories 38A are “weather”and “Islands”, while granular topic categories 38B of the article are“hurricanes”, “disaster”, and “Caribbean, for example. Based on thesetopic categories 38, the analysis engine 14 may determine that ademographic profile whose members are ages 35-55 would be interested insuch content, and that the news article may generate in those userspsychological states 42 of “fear” and “concern”, for instance. Ratherthan incorrectly associating the news article with an ad for a Caribbeanvacation package, which could be counterproductive, the semanticadvertiser system 10 may find ads that are linked to the identifieddemographic and/or the psychological states, such as an ad for hurricaneinsurance, for example.

According to one embodiment, one or both of the demographic profiles 40and the psychological states 42 may be linked to the additional contentbased on needs. For example, one or both of the of the demographicprofiles 40 in the demographic profile database 22 and the psychologicalstates 42 in the psychological state map 24 may be linked to a set ofneeds that are required or wanted by the corresponding demographicprofile and/or psychological state, as determined on a behavioral studyfor instance. The set of needs linked to each of the demographicprofiles 40 may be the same or different than the set of needs linked toeach of the psychological states 42. One or both sets of profile needsand state needs may then be linked to the contents in the additionalcontent database 26. Once the demographic profiles 40 and/or thepsychological states 42 are associated with the electronic text 28 basedon the assigned topic categories 38, the additional content 33 relatedto the electronic text 28 can be identified based in part on the needs.

Referring again to the exemplary embodiment of FIG. 1B, after using thegeneric topic categories 38A or the generic content map to query thecontextual taxonomy 20, the other attributes database 27, and thepsychological state map 24, the semantic engine 12 may create anenriched content map 50 containing extracted granular topic categories38B, other attributes 48, and needs 510. At least a part of the enrichedcontent map 50, such as the needs 510, may be used by the analysisengine 14 to identify a demographic profile 40 from the demographicprofile database 22, which in turn, may be used to present the user 32with additional content 33 from the additional content database 26specifically targeted to addressing the needs 510 associated with thedemographic profile 40 of the user 32 at that moment.

In the example above where the demographic profile ages 35-55 and thepsychological states “fear” and “concern” were identified, assume thatexample needs that are associated with the demographic profile and thepsychological states include “insurance” and “safety”. In oneembodiment, these same needs could be associated with a weight or scoreindicating a measure of importance to those needs with a particulardemographic profile and that demographic profile may be used to querythe additional content database 26 to find ads linked with thatdemographic profile. In another embodiment, these needs could be linkedto the ads for insurance, such as the ad for hurricane insurance and,the needs associated with demographic profile and/or the psychologicalstates may be used to extract ads with these same or similar needs.

The semantic advertiser system 10 that links document content not onlyto demographic profiles, but also to cultural or psychological statesoffers several advantages over conventional ad placement systems. Forcontent publishers, the semantic advertiser system 10 provides increasedvisibility of their content through real-time monitoring and detailedanalysis of their electronic texts. As a result ads are more relevantand publishers can generate increased revenue. The semantic advertisersystem 10 offer advertisers and advertising agencies the ability toreach target demographics more effectively by providing precise,real-time information about the content of an electronic text so morerelevant ads can be published, resulting in a higher return oninvestment. And for the user 32, the system 10 presents highly relevantadvertisements that are directly linked to the user's needs asdetermined by the user's psychological state caused by reading theelectronic text 28.

FIGS. 3A-3D are block diagrams illustrating three embodiments forlinking topic categories 38, psychological states 42, demographicprofiles 40, and additional content 33 using one or more databases. FIG.3A depicts a first embodiment in which the granular topic categories 38Bfrom the contextual taxonomy 20 are linked to the demographic profiles40 in the demographic profile database 22. The psychological states 42in the psychological state map 24 are linked to the demographic profiles40 in the demographic profile database 22. And the demographic profiles40 are then linked to the additional content 33 in the additionalcontent database 26. This is the embodiment shown in FIG. 1B.

FIG. 3B depicts a second embodiment in which the granular topiccategories 38B from the contextual taxonomy 20 are linked to both thedemographic profiles 40 in the demographic profile database 22 and thepsychological states 42 in the psychological state map 24. One or bothof the demographic profiles 40 and psychological states 42 may then belinked to the additional content 33 in the additional content database26.

FIG. 3C depicts a third embodiment in which the granular topiccategories 38B from the contextual taxonomy 20 are linked only to thedemographic profiles 40 in the demographic profile database 22. Thedemographic profiles 40 are linked to the psychological states 42 in thepsychological state map 24. And the psychological states 42 are linkedto the additional content 33 in the additional content database 26.

FIG. 3D depicts a fourth embodiment in which the granular topiccategories 38B from the contextual taxonomy 20 are linked only to thepsychological states 42 in the psychological state map 24. Thepsychological states 42 are linked to the demographic profiles 40 in thedemographic profile database 22. And the demographic profiles 40 arelinked to the additional content 33 in the additional content database26.

FIG. 4 is a block diagram graphically illustrating contents of thecontextual taxonomy 20. The contextual taxonomy may be a lexicaldatabase organized on a conceptual base (rather than alphabetical orderas a dictionary). The contextual taxonomy 20 may include a hierarchy ofindividual nodes 400 or records representing granular topic categories38B, where each node 400 is linked to zero- to-n sub-nodes 402 (e.g.,sub-categories), which may turn can have 0-to-n sub-nodes.

FIG. 5 is a diagram illustrating an example database schema according tothe first embodiment of FIGS. 1B and 3A, where like components have likereference numerals. The database schema includes the psychological statemap 24, the demographic profile table 22, and the additional contentdatabase 26.

The psychological state map 24 may be used to link the generic topiccategories 38A, also referred to as the generic content map 522, topsychological states 42. In one embodiment, for each psychological state42, the generic content map 522 comprises a specific combination of oneor more generic domains 528, main concepts 530, and entities 532. Eachof the psychological states 42 may also be linked to a set of 1-n needs44, where each need 44 may indicate a requirement or want of a person inthe corresponding psychological state 42.

The psychological state map 24 may be used to identify for theelectronic text 28, or a page thereof, a psychological state 42generated in the reader based on a specific combination of genericdomains 528, main concepts 530, and entities 532 generated from theelectronic text 28. The 1-m needs 44 linked to each psychological state42 may then be used to extract the demographic profile(s) 40 more proneto react to the need(s) 44.

The demographic profile table 22 may be used to link one or moregranular topic categories 38B from the contextual taxonomy 20 topredetermined demographic profiles 40. In one embodiment, thedemographic profile table 22 may be created or provided by the thirdparty entity 36. Each record in the demographic profile table 22 mayinclude the following fields: an enriched content map 50, a demographicprofile 40, and a weight 514.

The enriched content map 50 may include a granular topic category 38B, asentiment 506, other attributes 48, a set of 1-n needs 510, where eachneed 510 indicates a requirement or want of the correspondingdemographic profile 40. The records in the demographic profile table 22may link demographic profiles 40 to a specific combination of one ormore granular topic categories 38B, a sentiment 506, other customizableattributes 48, and the needs 510 in the enriched content map 50. Thesentiment 506 may be a demographic profile's attitude towards thecorresponding granular topic category 38B based mainly on emotionsinstead of reason (e.g., positive, neutral, negative).

The weight 514 associated with each of the demographic profiles 40 mayindicate a level of relative interest or importance of the correspondingenriched content map 50, particularly the granular topic category 38B,to the demographic profile 40 and the needs 510. The demographic profiletable 22 may be used to measure how relevant the electronic text 28, ora specific page thereof, is for each demographic profile 40, allowing asort the demographic profiles 40 based on how relevant the electronictext 28 (e.g., a website page) is for the specific demographic profile40.

In one embodiment, the set of needs 510 associated with the demographicprofiles 40 may be the same as the set of needs 44 associated with thepsychological states 42. In another embodiment, the set of needs 510associated with the demographic profiles 40 may be different than theset of needs 44 associated with the psychological states 42, but the twoset of needs 510 and 44 may overlap.

The additional content database 26 may comprise an inventory ofadditional content, such as advertisements, that is linked to thedemographic profiles 40 and that may be indirectly linked to thepsychological states 42. In one embodiment, the additional contentdatabase 26 may include the following fields: an advertiser 540, anadvertisement 546, and a demographic profile 40. The advertiser 540 maycomprise an identification of an advertiser who is supplying theadvertisement 546, which may be in the form of the actual advertisementor a pointer to a storage location of the advertisement.

In a further embodiment, each of the records in the additional contentdatabase 26 may further include need (not shown). These needs 44 maycorrespond to the needs 44 from the psychological state table 24 and/orto the needs 510 from the demographic profile table 22, allowing furtherflexibility in how the additional content database 26 is accessed. Thus,in one embodiment, the additional content database 26 may linkadditional content 33 such as advertisements 546, to both thedemographic profiles 40 and the needs 44 that address the psychologicalstates 42 of the reader, for instance.

FIG. 6 is a diagram illustrating a process of automatically identifyingcontent related to an electronic text in accordance with the embodimentshown in FIG. 3B. As described in the embodiment of FIG. 3B, one or moregranular topic categories 38B from the contextual taxonomy 20 are linkedto predetermined demographic profiles 40 and to psychological states 42;and one or both of the demographic profiles 40 and the psychologicalstates 42 are linked to the additional content.

Referring to FIGS. 3B, 5 and 6, in response to the semantic engine 12analyzing the electronic text 28 based on the contextual taxonomy 20 andassigning one or more of the granular topic categories 38B to theelectronic text 28, the analysis engine 14 assigns a hierarchy ofdemographic profiles 40 to the electronic text by searching thedemographic profile database 22 with the granular topic categories 38Bassigned to the electronic text 28 and retrieving the demographicprofiles 40 having a highest weight 514 for the assigned granular topiccategories 38B (block 600). The analysis engine 14 may also retrieve theneeds 510 and weights 514 of the retrieved demographic profiles 40 fromTable 2. In one embodiment, the semantic advertiser system 10 may usethe enriched content map 50 generated for the input electronic text 28to search the demographic profile table 22 and identify a correspondingdemographic profile 40.

The analysis engine 14 may assign at least one psychological state 42 toa reader of the electronic text 28 by searching the psychological statemap 24 with the granular topic categories 38B assigned to the electronictext 28 and retrieving the corresponding psychological state 42 (block602). The analysis engine 14 may also retrieve the needs 44 of theretrieved psychological state 42. In one embodiment, the semanticadvertiser system 10 may use the enriched content map 50 generated forthe input electronic text 28 to search the psychological state map 24and identify a psychological state generated in a reader correspondingto the content map.

The analysis engine 14 may identify additional content 33 from theadditional content database 26 based on at least one of the hierarchy ofdemographic profiles 40 or the set of needs 44 associated with theassigned psychological state 42 (block 604).

The additional content 33 may be identified using several embodiments.In one embodiment, the demographic profile 40 field of the additionalcontent database 26 may be searched with one of the demographic profiles40 retrieved from the demographic profile table 500 to retrieve one ormore corresponding advertisements 546. In this embodiment, the needs 44associated with the retrieved psychological state 42 may be used toextract the demographic profiles more prone to react to the needs, asindicated by the weights 514 associated with the demographic profileneeds 510.

In another embodiment where there is a needs field associated with theadditional content database 26, the additional content database 26 maybe searched with the set of needs 44 associated with the psychologicalstate 42 to retrieve one or more corresponding advertisements 546. Thisembodiment enables the system to work in the absence of availabledemographic profiles 40.

In yet another embodiment, the analysis engine 14 may consider thedemographic profiles 40, when available, to rank the needs 44 associatedwith the retrieved psychological states 42 prior to fetching theadditional content using the needs 44.

FIG. 7 is a diagram illustrating a process of automatically identifyingcontent related to an electronic text in accordance with the embodimentof FIG. 3C. As described in the embodiment of FIG. 3D, one or moregranular topic categories 38B from the contextual taxonomy 20 are linkedto the demographic profiles 40; the demographic profiles 40 are linkedto psychological states 42; and at least the psychological states 42 arelinked to the additional content.

Referring to FIGS. 3C and 7, in response to the semantic engine 12analyzing the electronic text 28 based on the contextual taxonomy 20 andassigning one or more of the granular topic categories 38B to theelectronic text 28, the analysis engine 14 automatically identifies atleast one of the demographic profiles 40 whose members would beinterested in the content that is linked to the one or more granulartopic categories 38B assigned to the content (block 700). The analysisengine 14 automatically identifies at least one of the psychologicalstates 42 of a user 32 likely to be caused by the content that is linkedto the at least one identified demographic profile (block 702). And theanalysis engine 14 presents a portion of the additional content 33targeted to addressing needs associated with the identifiedpsychological state of the user 32 (block 704).

FIG. 8 is a diagram illustrating a process of automatically identifyingcontent related to an electronic text in accordance with the embodimentof FIG. 3D. As described in the embodiment of FIG. 3D, one or moregranular topic categories 3B from the contextual taxonomy 20 are linkedto the psychological states; the psychological states are linked to thedemographic profiles; and the demographic profiles are linked to theadditional content.

Referring to FIGS. 3D and 8, in response to the semantic engine 12analyzing the electronic text 28 based on the contextual taxonomy 20 andassigning one or more of the granular topic categories 38B to theelectronic text 28, the analysis engine 14 automatically identifies atleast one of the psychological states 42 of a user 32 likely to becaused by the content that is linked to the one or more granular topiccategories 38B assigned to the content (block 800). The analysis engine14 automatically identifies at least one of the demographic profileswhose members would be interested in the content that is linked to theat least one identified psychological state (block 802). And theanalysis engine 14 automatically presents a portion of the additionalcontent 33 targeted to addressing needs associated with the identifiedpsychological state of the user 32 based on the identified demographicprofile (block 804).

As will be appreciated, in the embodiments described herein, the generictopic categories 38A may be used to access the demographic profiledatabase 22 and/or the psychological state map 24 instead of, or inaddition to, the granular topic categories 38B.

The operation of the semantic advertiser system 10 will be furtherdescribed with respect to the example described above of a web articleregarding President Obama throwing a first pitch at a major leaguebaseball game. As previously discussed, in response to processing thecontent of the article the semantic engine 12 may, among other things,generate a generic content map 522 as follows:

-   -   Generic Domain 528: Sport    -   Main Concepts 530: Barack Obama, pitch, Albert Pujols, MLB,        game, crowd    -   Entities 532:        -   People: Barack Obama, Albert Pujols, Jim McGraver        -   Organization: MLB, American League, National League        -   Geographic Location: Saint Louis        -   Sentiment: positive

At this point, the generic content map 522 is used as input to the datarepository 18 to:

-   -   access the contextual taxonomy 20 and build the enriched content        map 50 by associating to the article more granular topic        categories 38B (e.g., from sport to baseball.exhibition.game and        politics.internal-affair)    -   use the elements from the generic content map 522 to access the        additional attribute database 27 (e.g., Barack Obama is a symbol        of success, MLB of tradition, Saint Louis of middle America        values (or jazz) etc.). These other attributes 48 (success,        tradition) are included in the enriched content map 50    -   use the generic content map 522 to access the psychological        state map 24 to extract the psychological states 42 of a reader        (i.e. enjoyment, nostalgia) and the related needs 44 (eg.,        entertainment, coziness, family-love etc.)

After this process, the enriched content map 50 may be used by theanalysis engine 14 to query the demographic profile database 22 andretrieve the demographic profiles 40 having the highest weights 514measuring the relative importance or relevancy of the content in thetext 28 for each demographic profile 40.

The output of the semantic advertiser system 10 is a hierarchy ofdemographic profiles 40 that fit the content of the text 28. Thishierarchy of demographic profiles 40 can be used to access theadditional content database 26 or even the additional content inventory34. Either of these may be a third party inventory DB, such as GOOGLEADSENSE. In this way, the semantic advertiser system 10 can improve theperformance of other ad distribution servers, where the demographicprofiles 40 or even the psychological state are the keywords theadvertisements are associated with.

The value for an advertiser is that instead of buying the keyword“baseball”, the advertiser can buy the following output demographicprofiles: a demographic profile of “70+ year old men” that is triggeredonly when the topic category “baseball” is associated with psychologicalstate of “nostalgia”, for example, and the demographic profile of “40year old high income” that is triggered when the text 28 is associatedwith the attribute “success”, for example.

A method and system for automatically identifying content related to anelectronic text has been disclosed. The present invention has beendescribed in accordance with the embodiments shown, and there could bevariations to the embodiments, and any variations would be within thespirit and scope of the present invention. For example, the exemplaryembodiment can be implemented using hardware, software, a computerreadable medium containing program instructions, or a combinationthereof. Software written according to the present invention is to beeither stored in some form of computer-readable medium such as a memory,a hard disk, or a CD/DVD-ROM and is to be executed by a processor.Accordingly, many modifications may be made by one of ordinary skill inthe art without departing from the spirit and scope of the appendedclaims.

I claim:
 1. A computer-implemented method for automatically identifyingcontent related to an electronic text, comprising: linking topiccategories, psychological states, demographic profiles, and additionalcontent using one or more databases, wherein the topic categoriescomprise generic and granular topic categories, each of thepsychological states including a link to a first set of needs; inresponse to receiving content of the electronic text, analyzing by asoftware component executing on a computer the content and assigning oneor more of the topic categories to the electronic text; assigning ahierarchy of the demographic profiles to the electronic text bysearching a demographic profile database with the granular topiccategories assigned to the electronic text, each of the demographicprofiles having a weight indicating a level of relative importance ofthe corresponding granular topic categories to the demographic profile,and retrieving the demographic profiles having a highest weight for theassigned granular topic categories; assigning at least one psychologicalstate to a user of the electronic text by searching a psychologicalstate map with the granular topic categories assigned to the electronictext and retrieving the corresponding psychological state; andidentifying additional content from an additional content database basedon at least one of the hierarchy of demographic profiles and the set ofneeds associated with the assigned psychological state; and presentingat least a portion of the additional content.
 2. The method of claim 1wherein identifying at least one of the hierarchy of the demographicprofiles and the at least one of the psychological states comprises oneof identifying at least one of the hierarchy of the demographic profilesonly; identifying the at least one of the psychological states only; andidentifying at least one of each.
 3. The method of claim 1 furthercomprising linking at least one of the demographic profiles and thepsychological states to the additional content based on needs.
 4. Themethod of claim 1 further comprising: linking one or more of thegranular topic categories from a contextual taxonomy to thepsychological states; linking the one or more granular topic categoriesto the demographic profiles; and linking additional content to thedemographic profiles and the psychological states.
 5. An executablesoftware product comprising a non-transitory computer-readable mediumcontaining program instructions for automatically identifying contentrelated to an electronic text, the program instructions for: linking ina data repository topic categories, psychological states, demographicprofiles, and additional content using one or more databases, whereinthe topic categories comprise generic and granular topic categories,each of the psychological states including a link to a first set ofneeds; in response to a computer receiving content of the electronictext, analyzing by a software component executing on a the computer thecontent and assigning one or more of the topic categories to theelectronic text; assigning a hierarchy of the demographic profiles tothe electronic text by searching a demographic profile database with thegranular topic categories assigned to the electronic text, each of thedemographic profiles having a weight indicating a level of relativeimportance of the corresponding granular topic categories to thedemographic profile, and retrieving the demographic profiles having ahighest weight for the assigned granular topic categories; assigning atleast one psychological state to a user of the electronic text bysearching a psychological state map with the granular topic categoriesassigned to the electronic text and retrieving the correspondingpsychological state; and identifying additional content from anadditional content database based on at least one of the hierarchy ofdemographic profiles and the set of needs associated with the assignedpsychological state; and presenting at least a portion of the additionalcontent.
 6. A system, comprising: a memory; a processor coupled to thememory; a data repository stored in the memory, the data repositorycomprising: a taxonomy used to classify content of an electronic textinto one or more generic and granular topic categories; a demographicprofile database linking the one or more generic and granular topiccategories from the taxonomy to predetermined demographic profiles; apsychological state map linking the one or more generic and granulartopic categories to psychological states, each of the psychologicalstates including a link to a first set of needs; an additional contentdatabase linking additional content to at least one of the demographicprofiles and the psychological states; a semantic engine executing onthe processor configured to analyze the electronic text based on thetaxonomy and to assign one or more of the topic categories to theelectronic text; and an analysis engine executing on the processorconfigured to: assign a hierarchy of the demographic profiles to theelectronic text by searching a demographic profile database with thegranular topic categories assigned to the electronic text, each of thedemographic profiles having a weight indicating a level of relativeimportance of the corresponding granular topic categories to thedemographic profile, and retrieving the demographic profiles having ahighest weight for the assigned granular topic categories; assign atleast one psychological state to a user of the electronic text bysearching a psychological state map with the granular topic categoriesassigned to the electronic text and retrieving the correspondingpsychological state; and identify additional content from an additionalcontent database based on at least one of the hierarchy of demographicprofiles and the set of needs associated with the assigned psychologicalstate; and present at least a portion of the additional content.